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1. Spectroscopic optical coherence refraction tomography

   https://doi.org/10.1364/OL.389703  

   Zhou, Kevin_C; Qian, Ruobing; Farsiu, Sina; Izatt, Joseph_A (April 2020, Optics Letters)

   In optical coherence tomography (OCT), the axial resolution is often superior to the lateral resolution, which is sacrificed for long imaging depths. To address this anisotropy, we previously developed optical coherence refraction tomography (OCRT), which uses images from multiple angles to computationally reconstruct an image with isotropic resolution, given by the OCT axial resolution. On the other hand, spectroscopic OCT (SOCT), an extension of OCT, trades axial resolution for spectral resolution and hence often has superior lateral resolution. Here, we present spectroscopic OCRT (SOCRT), which uses SOCT images from multiple angles to reconstruct a spectroscopic image with isotropic spatial resolution limited by the OCTlateralresolution. We experimentally show that SOCRT can estimate bead size based on Mie theory at simultaneously high spectral and isotropic spatial resolution. We also applied SOCRT to a biological sample, achieving axial resolution enhancement limited by the lateral resolution. 

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2. miniMDS: 3D structural inference from high-resolution Hi-C data

   https://doi.org/10.1093/bioinformatics/btx271  

   Rieber, Lila; Mahony, Shaun (July 2017, Bioinformatics)

   Abstract MotivationRecent experiments have provided Hi-C data at resolution as high as 1 kbp. However, 3D structural inference from high-resolution Hi-C datasets is often computationally unfeasible using existing methods. ResultsWe have developed miniMDS, an approximation of multidimensional scaling (MDS) that partitions a Hi-C dataset, performs high-resolution MDS separately on each partition, and then reassembles the partitions using low-resolution MDS. miniMDS is faster, more accurate, and uses less memory than existing methods for inferring the human genome at high resolution (10 kbp). Availability and implementationA Python implementation of miniMDS is available on GitHub: https://github.com/seqcode/miniMDS. Supplementary informationSupplementary data are available at Bioinformatics online. 

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3. Asymmetric Synthesis of P-Stereogenic Secondary Phosphine ­Oxides (SPOs)

   https://doi.org/10.1055/a-1582-0169  

   Glueck, David S. (January 2022, Synthesis)

   Abstract P-Stereogenic secondary phosphine oxides [SPOs, RR′P(O)H], valuable ligands for metal complexes in asymmetric catalysis, are also building blocks for other chiral phosphorus derivatives. This short review summarizes methods used for asymmetric synthesis of P-stereogenic SPOs. 1 Introduction 2 Configurational Stability of P-Stereogenic SPOs 3 Classical Resolution, HPLC Separation, and Dynamic Resolution 4 Synthesis via Chiral Auxiliaries 5 Kinetic Resolution 6 Conclusions and Outlook 

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4. PSSR2: a user-friendly Python package for democratizing deep learning-based point-scanning super-resolution microscopy

   https://doi.org/10.1186/s44330-024-00020-5  

   Stites, Hayden_C; Manor, Uri (January 2025, BMC Methods)

   Abstract BackgroundTo address the limitations of large-scale high quality microscopy image acquisition, PSSR (Point-Scanning Super-Resolution) was introduced to enhance easily acquired low quality microscopy data to a higher quality using deep learning-based methods. However, while PSSR was released as open-source, it was difficult for users to implement into their workflows due to an outdated codebase, limiting its usage by prospective users. Additionally, while the data enhancements provided by PSSR were significant, there was still potential for further improvement. MethodsTo overcome this, we introduce PSSR2, a redesigned implementation of PSSR workflows and methods built to put state-of-the-art technology into the hands of the general microscopy and biology research community. PSSR2 enables user-friendly implementation of super-resolution workflows for simultaneous super-resolution and denoising of undersampled microscopy data, especially through its integrated Command Line Interface and Napari plugin. PSSR2 improves and expands upon previously established PSSR algorithms, mainly through improvements in the semi-synthetic data generation (“crappification”) and training processes. ResultsIn benchmarking PSSR2 on a test dataset of paired high and low resolution electron microscopy images, PSSR2 super-resolves high-resolution images from low-resolution images to a significantly higher accuracy than PSSR. The super-resolved images are also more visually representative of real-world high-resolution images. DiscussionThe improvements in PSSR2, in providing higher quality images, should improve the performance of downstream analyses. We note that for accurate super-resolution, PSSR2 models should only be applied to super-resolve data sufficiently similar to training data and should be validated against real-world ground truth data. 

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5. A Generative Super‐Resolution Model for Enhancing Tropical Cyclone Wind Field Intensity and Resolution

   https://doi.org/10.1029/2024JH000375  

   Lockwood, Joseph_W; Gori, Avantika; Gentine, Pierre (November 2024, Journal of Geophysical Research: Machine Learning and Computation)

   Abstract Extreme winds associated with tropical cyclones (TCs) can cause significant loss of life and economic damage globally, highlighting the need for accurate, high‐resolution modeling and forecasting for wind. However, due to their coarse horizontal resolution, most global climate and weather models suffer from chronic underprediction of TC wind speeds, limiting their use for impact analysis and energy modeling. In this study, we introduce a cascading deep learning framework designed to downscale high‐resolution TC wind fields given low‐resolution data. Our approach maps 85 TC events from ERA5 data (0.25° resolution) to high‐resolution (0.05° resolution) observations at 6‐hr intervals. The initial component is a debiasing neural network designed to model accurate wind speed observations using ERA5 data. The second component employs a generative super‐resolution strategy based on a conditional denoising diffusion probabilistic model (DDPM) to enhance the spatial resolution and to produce ensemble estimates. The model is able to accurately model intensity and produce realistic radial profiles and fine‐scale spatial structures of wind fields, with a percentage mean bias of −3.74% compared to the high‐resolution observations. Our downscaling framework enables the prediction of high‐resolution wind fields using widely available low‐resolution and intensity wind data, allowing for the modeling of past events and the assessment of future TC risks. 

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